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February 2, 2026Mathematical Methods in the Applied Sciences1 citations

Application of Multivariate Bilinear Neural Network Method to a Spatial Symmetric Nonlinear Dispersive Wave Model in (2 + 1)‐Dimensions

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HWHai‐Peng WangJiangxi University of Traditional Chinese MedicineQYQing YeJiangxi University of Traditional Chinese MedicineZZZhenhui ZhangJiangxi University of Traditional Chinese Medicine

Key Points

  • This research aims to derive exact analytical solutions for nonlinear partial differential equations using a neural network method.
  • Applied multivariate bilinear neural network method (MBNNM) to nonlinear partial differential equations.
  • Developed a new MBNNM architecture (3-4-2-1) alongside existing architectures.
  • Utilized generalized activation functions to obtain diverse exact analytical solutions.
  • Employed 3D/2D plots, contour plots, and density maps to characterize dynamic behaviors.
  • Achieved multiple exact analytical solutions for the spatial symmetric nonlinear dispersive wave model.
  • Demonstrated improved computational efficiency through a novel matrix-based solution strategy.
  • Transformed Hirota bilinear expansion into matrices, enhancing performance in solving complex equations.

Abstract

ABSTRACT In this work, the multivariate bilinear neural network method (MBNNM) is applied to derive exact analytical solutions for nonlinear partial differential equations (NPDEs). Specifically, a (2 + 1)‐dimensional spatial symmetric nonlinear dispersive wave model (SSDWM) is investigated by integrating MBNNM with established architectures (3‐2‐2‐1, 3‐2‐3‐1, and 3‐3‐2‐1), while a new 3‐4‐2‐1 architecture is developed for further investigation. By systematically selecting generalized activation functions, diverse exact analytical solutions are obtained, with their dynamic behaviors characterized via 3D/2D plots, contour plots, and density maps. To improve the computational efficiency of MBNNM in handling complex equations, a novel matrix‐based solution strategy is proposed. This strategy significantly enhances computational performance by transforming the Hirota bilinear expansion into matrices for arithmetic processing.

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Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/6980ffd6c1c9540dea812a06https://doi.org/10.1002/mma.70516
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